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Approximate data instance matching: a survey

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Abstract

Approximate data matching is a central problem in several data management processes, such as data integration, data cleaning, approximate queries, similarity search and so on. An approximate matching process aims at defining whether two data represent the same real-world object. For atomic values (strings, dates, etc), similarity functions have been defined for several value domains (person names, addresses, and so on). For matching aggregated values, such as relational tuples and XML trees, approaches alternate from the definition of simple functions that combine values of similarity of record attributes to sophisticated techniques based on machine learning, for example. For complex data comparison, including structured and semistructured documents, existing approaches use both structure and data for the comparison, by either considering or not considering data semantics. This survey presents terminology and concepts that base approximated data matching, as well as discusses related work on the use of similarity functions in such a subject.

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  1. Centro Tecnologico (CTC), Depto. Informatica e Estatistica (INE), Universidade Federal de Santa Catarina (UFSC), Campus Universitario Trindade, Florianopolis, SC, Brazil

    Carina Friedrich Dorneles, Rodrigo Gonçalves & Ronaldo dos Santos Mello

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  1. Carina Friedrich Dorneles
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Correspondence to Carina Friedrich Dorneles.

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This work was partially done while the author C. F. Dorneles was a Ph.D. student at the Informatic Institute, UFRGS, and was supported by Capes.

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Dorneles, C.F., Gonçalves, R. & dos Santos Mello, R. Approximate data instance matching: a survey. Knowl Inf Syst 27, 1–21 (2011). https://doi.org/10.1007/s10115-010-0285-0

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